The present invention relates generally to electronic sensors and instrumentation for measuring the velocity of moving media. More specifically, the present invention relates to Laser Doppler Velocimeters, and methods for processing the signals received therefrom.
Laser Doppler Velocimeters (LDVs) are commonly used in many industrial and related applications. Laser Doppler Velocimeters (LDVs) operate on the principle that when a laser beam scatters off a moving object, the frequency of oscillation is altered by the Doppler effect. In operation, LDVs often provide two crossing laser beams from the same laser source, with a small angle therebetween. The measurement volume where the velocity information is obtained is defined by the region in space where the two beams cross. The light scattered from the first beam is Doppler shifted slightly higher in frequency because the object is moving toward the beam. Likewise, light scattered from the second beam is Doppler shifted slightly lower in frequency because the object is moving away from the beam. The scattered light signal is gathered using a collection lens, and when the two scattered signals are mixed together on a photo diode, a difference frequency can be obtained. The velocity of the object is directly proportional to the difference frequency.
LDV techniques have been used in many industrial applications including paper manufacturing. These systems tend to be relatively complex, expensive, and difficult to install and operate unattended for extended periods. Accordingly, LDV systems are often used for machine diagnostics purposes only.
Simple counters or phase lock loop type signal processors have been used to process LDV signals obtained from lightly seeded flow experiments where it is likely that only a single scattering particle will be present in the LDV measurement volume at any given time. However, for more heavily seeded flows, more than one scattering particle are likely to be present in the measurement volume. The location and distribution of these particles is random. The light scattering effects of two particles can add constructively to give rise to a larger signal, or add destructively to give rise to a smaller signal, often resulting in a significant amount of phase noise.
To deal with heavily seeded flows, previous devices often use autocorrelation or Fast Fourier Transforms (FFTs) to process the raw signals coming from the Laser Doppler Velocimeter to extract the desired frequency signal from the phase and amplitude noise. Both of these methods are computationally expensive, requiring rather powerful processors and large amounts of memory to effectively process the raw input signals. For example, a 5 MHz Doppler signal requires a sampling rate of at least two, and more likely three to four times, the 5 MHz Doppler signal to satisfy the Nyquist criteria. Thus, a 10-20 MHz sampling rate may be required, leading to a large number of data points to be processed.
When the LDV is used as a sensor in closed loop control of a machine or process having a short time constant, faster sampling rates may be required to properly control the machine or process. For example, 10 velocity samples per second may be needed to effectively control a fast machine or process, thus requiring 10 FFTs per second on a large number of data points. In terms of current technology, this might require a processor on the order of a 500 MHz Pentium III ((copyright) Intel Corporation).
Fast processors can be expensive relative to the desired cost of the sensor system as a whole. A fast processor can also require supporting chips and present heat dissipation problems, particularly in industrial environments where the ambient temperature is high.
What would be desirable, therefore is a method and apparatus for processing LDV signals that requires substantially less computational resources as FFT or autocorrelation methods. This may lower the cost and increase the reliability of LDV systems, particularly those used in harsh industrial environments.
The present invention includes signal processing methods for processing Laser Doppler Velocimeter (LDV) signals, and computer programs, signal processors, and LDV systems incorporating these methods. The methods provide signal processing for extracting the true Doppler difference frequency from the phase noise frequencies present in many differential Doppler signals, which can then be used to determine the velocity of the moving media.
It has been found that the highest frequency present in an LDV signal corresponds to the velocity of the medium. The Doppler phase noise is typically at random, but lower, frequencies. To identify the highest frequency, one illustrative method includes identifying the frequency in a current LDV signal, and replacing that frequency when any subsequent higher frequency is received and detected. This is continued for a predetermined time period. To allow for changes in the velocity of the medium, the highest detected frequency is released after a certain age or lifetime. The duration of the age or lifetime can be programmed to accommodate the desired response time of the system.
Preferably, the highest detected frequency for each lifetime is stored for a predetermined time, and the highest frequency readings over a window of lifetimes are averaged to provide a moving or rolling average value. This moving or rolling average can be thought of as the output of a first xe2x80x9caveragingxe2x80x9d filter. One or more other filter layers may also be provided. For example, a second filter may provide a moving or rolling average of the output values of the first filter. That is, the second filter may average the results of the first filter until it is time to output a result. A simple summation and averaging of the first filter output may be performed at a configurable update frequency. In a preferred embodiment, the update frequency is about 10 times per second.
To eliminate gross errors, the highest detected frequency for each lifetime may be compared to the highest detected frequency of the last or other lifetimes, and rejected if the change is too great. Since the highest detected frequency values can be filtered by, for example a rolling average filter, any gross errors may disproportionately skew the rolling average values.